Systems and methods for automated detection and segmentation of vertebral centrum(s) in 3D images

ABSTRACT

Presented herein are systems and methods that allow for vertebral centrums of individual vertebrae to be identified and segmented within a 3D image of a subject (e.g., a CT or microCT image). In certain embodiments, the approaches described herein identify, within a graphical representation of an individual vertebra in a 3D image of a subject, multiple discrete and differentiable regions, one of which corresponds to a vertebral centrum of the individual vertebra. The region corresponding to the vertebral centrum may be automatically or manually (e.g., via a user interaction) classified as such. Identifying vertebral centrums in this manner facilitates streamlined quantitative analysis of 3D images for osteological research, notably, providing a basis for rapid and consistent evaluation of vertebral centrum morphometric attributes.

RELATED APPLICATION DATA

This application is a National Stage Application under 35 U.S.C. 371 ofco-pending PCT application number PCT/US2018/025383 designating theUnited States and filed Mar. 30, 2018; which is incorporated herein byreference in its entirety.

FIELD OF THE INVENTION

This invention relates generally to methods and systems of imageprocessing and analysis. More particularly, in certain embodiments, theinvention relates to automatic detection and/or segmentation ofvertebral centrum(s) in an anatomical image of a small subject (e.g.,small animal; e.g., small mammal), e.g., captured with a computedtomography (CT) scanner.

BACKGROUND OF THE INVENTION

There is a wide array of technologies directed to in vivo imaging ofmammals—for example, bioluminescence, fluorescence, tomography, andmultimodal imaging technologies. In vivo imaging of small mammals isperformed by a large community of investigators in various fields, e.g.,oncology, infectious disease, and drug discovery.

In vivo micro computed tomography (hereafter, “microCT”) imaging, is anx-ray-based technology that can image tissues, organs, and non-organicstructures with high resolution, although higher-throughput imaging maymake beneficial use of lower resolution microCT imaging to speed imageacquisition and/or processing while maintaining acceptable accuracy andimage detail. MicroCT has evolved quickly, requiring low dose scanningand fast imaging protocols to facilitate multi-modal applications andenable longitudinal experimental models. In vivo imaging often involvesthe use of reagents, such as fluorescent probes, for non-invasivespatiotemporal visualization of biological phenomena inside a liveanimal. Multi-modal imaging involves the fusion of images obtained indifferent ways, for example, by combining FMT, PET, MRI, CT, and/orSPECT imaging data.

Image analysis applications and/or imaging systems generally allow forvisualization, analysis, processing, segmentation, registration, andmeasurement of biomedical images. These applications and systems alsoprovide volume rendering tools (e.g., volumetric compositing, depthshading, gradient shading, maximum intensity projection, summed voxelprojection, signal projection); manipulation functions (e.g., to defineareas of structures of interest, delete unwanted objects, edit imagesand object maps); and measurement functions (e.g., for calculation ofnumber of surface voxels, number of exposed faces, planar area of aregion, and estimated surface area or volume of a region).

Image segmentation techniques are often used to identify separateregions of images that correspond to different structures, organs,and/or tissue of interest. Where different structures of interest aresimilar in nature and/or found in close proximity to each other,accurate and robust image segmentation can be challenging. Inparticular, while segmenting representations of individual bones (e.g.,to differentiate between individual bones) is sufficiently difficult inand of itself, further segmentation of individual bones in order todifferentiate between their various sub-regions can present even greaterchallenges. For example, while individual bones are naturally physicallyseparated from each other at joints, physical structural divisionsbetween various sub-regions of specific bones are often not clearlydiscernable. Further segmenting individual bones in this manner (e.g.,to differentiate between various sub-regions of a specific bone),however, is valuable for imaging approaches directed to the study and/ordiagnosis of bone formation, injury, and disease.

For example, osteological research often involves quantitative analysisof bone morphometric attributes. Studies focusing on vertebral boneformation, spine injuries, and diseases such as degenerative discdisease and osteoporosis measure morphometric attributes of vertebraeand specific sub-regions thereof in in order to gauge, for example,disease state and/or progression, injury severity, and the like.Measurement and analysis of vertebrae morphometric attributes typicallyfocus on a specific portion of each vertebrae, referred to as thevertebral centrum or vertebral body. The vertebral centrum is a thickoval-shaped central portion of an individual vertebra, comprisingcancellous bone tissue encircled by a protective layer of compact bone,which forms a cortical compartment. Structures referred to as pediclesprotrude from each side of the vertebral centrum and join with laminaeto form a vertebral arch. Vertebral centrums are major load bearingstructures in vertebrae and are prone to developing compressionfractures, particularly in patients with degenerative diseases such asosteoporosis. Accordingly, measurement and analysis of vertebral centrumregions of vertebrae are especially significant for osteologicalresearch and/or diagnosis.

Ex vivo and/or in vivo measurements of bone morphometric attributes areoften obtained using microCT imaging, which provides sufficient contrastbetween bone and soft-tissue. An example microCT image of severalvertebrae is shown in FIG. 1A and FIG. 1B. In FIG. 1B, the vertebralcentrums of each of three vertebrae are manually identified. Analysis ofmicroCT images to measure morphometric attributes of vertebralcentrum(s) can provide insight useful for developing understanding ofdisease and/or injury diagnosis, state, and progression in a subject, aswell as analysis of efficacy of different treatments. However,heretofore, image-based analysis of vertebrae morphometric attributeshave relied on manual identification of vertebral centrums (e.g., viahand-drawn boundaries drawn by a user, as shown in FIG. 1B).

Accordingly, there exists a need for improved systems and methods forautomated segmentation of individual bones into their variousconstituent sub-regions. In particular, there exists a need for systemsand methods that can automatically identify vertebral centrums ofindividual vertebrae.

SUMMARY OF THE INVENTION

Presented herein are systems and methods that allow for vertebralcentrums of individual vertebrae to be identified and segmented within a3D image of a subject (e.g., a CT or microCT image). In certainembodiments, the approaches described herein identify, within agraphical representation of an individual vertebra in a 3D image of asubject, multiple discrete and differentiable regions, one of whichcorresponds to a vertebral centrum of the individual vertebra. Theregion corresponding to the vertebral centrum may be automatically ormanually (e.g., via a user interaction) classified as such. Identifyingvertebral centrums in this manner facilitates streamlined quantitativeanalysis of 3D images for osteological research, notably, providing abasis for rapid and consistent evaluation of vertebral centrummorphometric attributes.

In certain embodiments, to provide for accurate and robustidentification and segmentation of vertebral centrum regions of images,the approaches described herein utilize a series of image processingsteps that account for and leverage insight about the specific physicalstructure of individual vertebrae and vertebral centrums thereof.

In certain embodiments, a single vertebra mask that identifies a portionof a 3D image that corresponds to particular individual vertebra ofinterest is first accessed and/or generated. A series of specific imageprocessing steps are then applied to this single vertebra mask toseparate out a vertebral centrum sub-region that corresponds to thevertebral centrum of the vertebra of interest. The specific imageprocessing steps used leverage insight regarding the specific physicalgeometry of vertebrae and the manner in which the vertebral centrum isphysically differentiated from the other regions of individualvertebrae. The approaches herein include steps that not only takeadvantage of the manner in which these physical features are representedin images to provide for segmentation, but also address sources ofsevere errors that result from image features that correspond to certainphysical structures of vertebrae.

In particular, FIG. 2A and FIG. 2B show external and cross sectionalviews of individual vertebra. As described herein, and shown in FIG. 2A,externally, the vertebral centrum appears to be a thick, oval-shapedsolid bone structure, from which narrower pedicles protrude. Acombination of distance transform and subsequent watershed segmentationoperations can be used to sub-divide graphical representations, such asmasks, at points where they narrow. Accordingly, such a combination ofsteps offers potential to separate the vertebral centrum region fromother regions of a single vertebra mask based on the apparent thicknessof the vertebral centrum in comparison with the narrower connectingregions that join it to other parts of the vertebra.

However, as shown in FIG. 2A and FIG. 2B, while the vertebral centrum202 appears solid externally, its interior (e.g., trabecular portion)252 (not to be confused with the neural canal 204) comprises finestructure and cavities occupied by marrow and soft-tissue. Accordingly,in certain embodiments, single vertebra masks generated from 3D imagesto identify individual vertebrae have a hollow, shell-like structuresthat represent image regions that correspond to solid bone, with regionscorresponding to soft-tissue and marrow are typically omitted.Additionally, single vertebra masks often include perforations that runbetween the interior (e.g., cavity corresponding to a marrow and/orsoft-tissue region) and exterior of the mask. These perforationscorrespond physically to common physical structures, such as bloodvessels within bones, as well as other physical structures, such astumors and/or cracks. Features such as tumors and cracks, though lesscommon in general, may be present in vertebrae of subjects imaged forosteological applications related to analysis and/or diagnosis ofcertain diseases or injuries.

In certain embodiments, the hollow cavities and/or perforations insingle vertebra masks prevent the above described distance transform andwatershed segmentation operations from accurately and robustly detectingand segmenting the vertebral centrum region of a single vertebra mask.In particular, perforations and hollow regions within a single vertebramask can create numerous narrow regions within the vertebral centrumregion itself. With numerous narrow features within the vertebralcentrum region itself, the narrow connections that also exist betweenthe vertebral centrum and other vertebra regions fail to providespecificity for separating between the vertebral centrum and otherregions. Accordingly, distance transform and watershed segmentationoperations applied to such as mask can produce significantover-segmentation errors, indistinguishably sub-dividing the singlevertebra mask at narrow features within the vertebral centrum region inaddition to at its connections to other regions.

Accordingly, in certain embodiments, in order to address this challenge,the vertebral centrum segmentation approaches described herein utilize afilling step that artificially fills in regions of a single vertebramask that correspond to perforations and interior (e.g., trabecular)regions, such as region 252 in FIG. 2B. This approach transforms theindividual single vertebral mask from a shell-like structure to a solidstructure—a filled single vertebra mask. Applying the distance transformand watershed segmentation steps to the filled single vertebra mask, asopposed to the initial single vertebra mask, allows them to successfullytake advantage of the narrow connections between the vertebral centrumand other regions of the individual vertebra to accurately and robustlydetect and segment the vertebral centrum region. Including such afilling step prior to performing the distance transform and watershedsegmentation steps thus accounts for the unique physical geometry ofvertebrae and avoids over-segmentation errors that would otherwiseresult from the hollow and/or perforated initially obtained (e.g.,generated; e.g., accessed) single vertebra mask.

In certain embodiments, identified vertebral centrum regions can be usedto perform quantitative measurements of volume, surface (e.g., surfacearea), connectivity, and other morphometric attributes of trabecular andcortical compartments of vertebral centrums of vertebrae. Suchmeasurements serve as valuable metrics for, for example, assessingdisease state in a subject and may be performed repeatedly over time toevaluate disease progression and treatment efficacies. For example,automated quantification of trabecular volume in longitudinal studiescan provide insight into efficacy of different treatments for vertebralosteoporosis.

Notably, previous approaches for measurements of morphometric attributesthat rely on manual identification of vertebral centrums, for examplevia hand-drawn boundaries as shown in FIG. 1B, are cumbersome and proneto human error and inconsistency. In contrast, by automaticallyidentifying vertebral centrum sub-regions, which, at most need to merelybe classified [e.g., via a single ‘affirmative’ click (e.g., as a mousebased interface) or tap (e.g., via a touch-sensitive interface)] by auser, the systems and methods provided herein dramatically streamlineimage analysis, allowing for more accurate and consistent analysis to beperformed rapidly. By improving the accuracy and rate of analysis inthis manner, the systems and methods described herein provide a valuabletool for osteological research and diagnosis.

In one aspect, the invention is directed to a method for automaticallydetecting and segmenting a vertebral centrum of a particular vertebra ina 3D image of a subject (e.g., an anatomical image of the subject), themethod comprising: (a) receiving, by a processor of a computing device,a 3D image of a subject [e.g., wherein the image is an anatomical image(e.g., a CT image, e.g., a microCT image)], wherein the 3D imagecomprises a graphical representation of one or more vertebra portions ofthe subject; (b) accessing and/or generating, by the processor, a singlevertebra mask that identifies a portion of the graphical representationdetermined as corresponding to the particular vertebra [e.g., whereinthe single vertebra mask is a binary mask comprising a plurality ofvoxels, each single vertebra mask voxel corresponding to a voxel of thereceived 3D image, wherein single vertebra mask voxels identified ascorresponding to the particular vertebra are assigned a first value(e.g., a numeric 1; e.g., a Boolean ‘true’) and other voxels (e.g.,identified as not corresponding to the particular vertebra) are assigneda second value (e.g., numeric 0; e.g., Boolean ‘false’)]; (c) applying,by the processor, one or more morphological operations (e.g.,morphological dilation; e.g., morphological hole filling; e.g.,morphological erosion) to fill in perforations and/or one or moreinterior regions of the single vertebra mask, thereby generating afilled single vertebra mask; (d) determining, by the processor, adistance map by applying a distance transform to the filled singlevertebra mask [e.g., wherein the distance map comprises a plurality ofdistance map voxels, each of which corresponds to a voxel of the filledsingle vertebra mask and has (e.g., is assigned) a distance value thatrepresents a distance from the voxel to a nearest boundary and/ornon-bone voxel (e.g., a voxel of the filled single vertebra mask havinga value of 0)]; (e) applying, by the processor, a watershed segmentationoperation to the distance map to identify a set of catchment basins fromthe distance map [e.g., by portioning the distance map into a pluralityof catchment basins that are separated from each other by watershedlines; e.g., wherein the watershed segmentation operation produces awatershed mask comprising a plurality of catchment basins (e.g., eachcatchment basin corresponding to a connected region of voxels assigned afirst value such as a numeric 1 or Boolean ‘true’) separated from eachother by watershed lines (e.g., each watershed line corresponding to aconnected line of voxels assigned a second value, such as a numeric 0 orBoolean ‘true’)]; (f) determining, by the processor, using the set ofcatchment basins and the single vertebra mask, a labeled inter-segmentedvertebra map comprising a plurality of labeled regions, one of whichcorresponds to the vertebral centrum [e.g., the labeled inter-segmentedvertebra map corresponding to a labeled version of the single vertebramask in which portions of the single vertebra mask lying withindifferent catchment basins of the set of catchment basins are identified(e.g., by taking a logical AND of each catchment basin of the set ofcatchment basins and the single vertebra mask) and labeled accordinglyto distinguish them from each other]; and (g) rendering, by theprocessor, a graphical representation of the labeled inter-segmentedvertebra map [e.g., for display to a user; e.g., wherein the graphicalrepresentation visually distinguishes differently labeled regions of thelabeled vertebra map (e.g., using different colors, shadings, etc.)].

In certain embodiments, step (b) comprises segmenting, by the processor,the 3D image to generate the single vertebra mask.

In certain embodiments, step (b) comprises: segmenting, by theprocessor, the 3D image to generate a labeled (segmented) bone mapcomprising a plurality of labeled regions that differentiate portions ofthe graphical representation corresponding to individual bones (e.g.,including, but not limited to the one or more vertebra portions; e.g.,each labeled region of the labeled (segmented) bone map corresponding toa portion of the graphical representation determined as corresponding toa particular individual bone); rendering, by the processor, a graphicalrepresentation of the labeled (segmented) bone map [e.g., for display toa user; e.g., wherein the graphical representation visuallydistinguishes differently labeled regions of the labeled (segmented)bone map (e.g., using different colors, shadings, etc.)]; receiving, bythe processor, a user selection of at least one of the plurality oflabeled regions; and generating, by the processor, the single vertebramask from the user selected labeled region.

In certain embodiments, the segmenting the 3D image comprises applyingone or more second derivative splitting filters to the 3D image [e.g.,applying one or more second derivative splitting filters to the image toproduce a split bone mask for the image with bone boundaries removed;determining a plurality of split binary components of the split bonemask by performing one or more morphological processing operations; andperforming a region growing operation using the split binary componentsof the split bone mask as seeds, thereby producing the labeled(segmented) bone map comprising the plurality of labeled regions thatdifferentiate individual bones in the 3D image].

In certain embodiments, at least a portion of the single vertebra masklies on an edge of the 3D image, and the method comprises filling aninterior of the portion of the single vertebra mask lying on the edge ofthe 3D image.

In certain embodiments, step (c) comprises: applying, by the processor,a morphological dilation operation to grow the single vertebra mask(e.g., to fill in perforations in the single vertebra mask), therebygenerating a dilated single vertebra mask; and applying, by theprocessor, a morphological hole filling operation to the dilated singlevertebra mask to fill one or more interior regions within the dilatedsingle vertebra mask to generate the filled single vertebra mask.

In certain embodiments, the method comprises refining the filled singlevertebra mask by performing, by the processor, a morphological erosionoperation (e.g., using a morphological erosion element having a sizethat is the same and/or approximately equal to a size of a morphologicaldilation element used in the morphological dilation operation).

In certain embodiments, the morphological dilation operation uses adilation element having a preset and/or automatically determined sizebased on a resolution of the 3D image [e.g., such that the dilationelement size corresponds to a particular physical size based on (e.g.,approximately equal to; e.g., slightly larger than) one or more physicalfeatures associated with holes running from exterior to interior ofvertebra bones (e.g., blood vessels within vertebra)(e.g., ranging from100 to 240 microns along each dimension)]. In certain embodiments, themethod comprises receiving, by the processor, a user input of a dilationelement size value and using the user input dilation element size in theapplying the morphological dilation operation (e.g., such that the usercan enlarge the dilation element size to account for uncommon featuressuch as cracks, tumors, etc. in imaged vertebrae).

In certain embodiments, the method comprises: (h) following step (g),receiving, by the processor, via a graphical user interface (GUI), auser selection of the labeled region of the inter-segmented vertebra mapthat corresponds to the vertebral centrum; and (i) determining, by theprocessor, a vertebral centrum region of the inter-segmented vertebramap, the vertebral centrum region corresponding to the user selection[e.g., (A) by labeling, by the processor, the user selected labeledregion as corresponding to the vertebral centrum (e.g., and labeling, bythe processor, the remaining labeled regions as corresponding to otherregions of the vertebra), thereby producing a labeled vertebral centrummap (e.g., a binary map) that differentiates a region of the singlevertebra mask corresponding to the vertebral centrum from other regionsof the single vertebral mask; e.g., (B) by generating, by the processor,a vertebral centrum mask that identifies the labeled region selected bythe user].

In certain embodiments, the method comprises determining, by theprocessor, one or more morphometric measurements (e.g., for diagnosticpurposes; e.g., for determining treatment efficacy) using the determinedvertebral centrum region.

In certain embodiments, the one or more morphometric measurementscomprise measurements of one or more morphometric attributes of atrabecular and/or cortical component of the vertebral centrum (e.g., avolume of a trabecular component of the vertebral centrum).

In certain embodiments, the 3D image of the subject is a CT image (e.g.,a microCT image) and the method comprises acquiring the CT image (e.g.,the microCT image).

In another aspect, the invention is directed to a system forautomatically detecting and segmenting a vertebral centrum of aparticular vertebra in a 3D image of a subject (e.g., an anatomicalimage of the subject), the system comprising: a processor of a computingdevice; and a memory having instructions stored thereon, wherein theinstructions, when executed by the processor, cause the processor to:(a) receive a 3D image of a subject [e.g., wherein the image is ananatomical image (e.g., a CT image, e.g., a microCT image)], wherein the3D image comprises a graphical representation of one or more vertebraportions of the subject; (b) access and/or generate a single vertebramask that identifies a portion of the graphical representationdetermined as corresponding to the particular vertebra [e.g., whereinthe single vertebra mask is a binary mask comprising a plurality ofvoxels, each single vertebra mask voxel corresponding to a voxel of thereceived 3D image, wherein single vertebra mask voxels identified ascorresponding to the particular vertebra are assigned a first value(e.g., a numeric 1; e.g., a Boolean ‘true’) and other voxels (e.g.,identified as not corresponding to the particular vertebra) are assigneda second value (e.g., numeric 0; e.g., Boolean ‘false’)]; (c) apply oneor more morphological operations (e.g., morphological dilation; e.g.,morphological hole filling; e.g., morphological erosion) to fill inperforations and/or one or more interior regions of the single vertebramask, thereby generating a filled single vertebra mask; (d) determine adistance map by applying a distance transform to the filled singlevertebra mask [e.g., wherein the distance map comprises a plurality ofdistance map voxels, each of which corresponds to a voxel of the filledsingle vertebra mask and has (e.g., is assigned) a distance value thatrepresents a distance from the voxel to a nearest boundary and/ornon-bone voxel (e.g., a voxel of the filled single vertebra mask havinga value of 0)]; (e) apply a watershed segmentation operation to thedistance map to identify a set of catchment basins from the distance map[e.g., by portioning the distance map into a plurality of catchmentbasins that are separated from each other by watershed lines; e.g.,wherein the watershed segmentation operation produces a watershed maskcomprising a plurality of catchment basins (e.g., each catchment basincorresponding to a connected region of voxels assigned a first valuesuch as a numeric 1 or Boolean ‘true’) separated from each other bywatershed lines (e.g., each watershed line corresponding to a connectedline of voxels assigned a second value, such as a numeric 0 or Boolean‘true’)]; (f) determine, using the set of catchment basins and thesingle vertebra mask, a labeled inter-segmented vertebra map comprisinga plurality of labeled regions, one of which corresponds to thevertebral centrum [e.g., the labeled inter-segmented vertebra mapcorresponding to a labeled version of the single vertebra mask in whichportions of the single vertebra mask lying within different catchmentbasins of the set of catchment basins are identified (e.g., by taking alogical AND of each catchment basin of the set of catchment basins andthe single vertebra mask) and labeled accordingly to distinguish themfrom each other]; and (g) render a graphical representation of thelabeled inter-segmented vertebra map [e.g., for display to a user; e.g.,wherein the graphical representation visually distinguishes differentlylabeled regions of the labeled vertebra map (e.g., using differentcolors, shadings, etc.)].

In certain embodiments, at step (b), the instructions cause the processto segment the 3D image to generate the single vertebra mask.

In certain embodiments, at step (b), the instructions cause theprocessor to: segment the 3D image to generate a labeled (segmented)bone map comprising a plurality of labeled regions that differentiateportions of the graphical representation corresponding to individualbones (e.g., including, but not limited to the one or more vertebraportions; e.g., each labeled region of the labeled (segmented) bone mapcorresponding to a portion of the graphical representation determined ascorresponding to a particular individual bone); render a graphicalrepresentation of the labeled (segmented) bone map [e.g., for display toa user; e.g., wherein the graphical representation visuallydistinguishes differently labeled regions of the labeled (segmented)bone map (e.g., using different colors, shadings, etc.)]; receive a userselection of at least one of the plurality of labeled regions; andgenerate the single vertebra mask from the user selected labeled region.

In certain embodiments, the instructions cause the processor to segmentthe 3D image by applying one or more second derivative splitting filtersto the 3D image [e.g., by: applying one or more second derivativesplitting filters to the image to produce a split bone mask for theimage with bone boundaries removed; determining a plurality of splitbinary components of the split bone mask by performing one or moremorphological processing operations; and performing a region growingoperation using the split binary components of the split bone mask asseeds, thereby producing the labeled (segmented) bone map comprising theplurality of labeled regions that differentiate individual bones in the3D image].

In certain embodiments, at least a portion of the single vertebra masklies on an edge of the 3D image, and wherein the instructions cause theprocessor to fill an interior of the portion of the single vertebra masklying on the edge of the 3D image.

In certain embodiments, at step (c), the instructions cause theprocessor to: apply a morphological dilation operation to grow thesingle vertebra mask (e.g., to fill in perforations in the singlevertebra mask), thereby generating a dilated single vertebra mask; andapply a morphological hole filling operation to the dilated singlevertebra mask to fill one or more interior regions within the dilatedsingle vertebra mask to generate the filled single vertebra mask.

In certain embodiments, the instructions cause the processor to refinethe filled single vertebra mask by performing a morphological erosionoperation (e.g., using a morphological erosion element having a sizethat is the same and/or approximately equal to a size of a morphologicaldilation element used in the morphological dilation operation).

In certain embodiments, the instructions cause the processor to performthe morphological dilation operation using a dilation element having apreset and/or automatically determined size based on a resolution of the3D image [e.g., such that the dilation element size corresponds to aparticular physical size based on (e.g., approximately equal to; e.g.,slightly larger than) one or more physical features associated withholes running from exterior to interior of vertebra bones (e.g., bloodvessels within vertebra)(e.g., ranging from 100 to 240 microns alongeach dimension)].

In certain embodiments, the instructions cause the processor to receivea user input of a dilation element size value and use the user inputdilation element size in the applying the morphological dilationoperation (e.g., such that the user can enlarge the dilation elementsize to account for uncommon features such as cracks, tumors, etc. inimaged vertebrae).

In certain embodiments, the instructions cause the processor to: (h)following step (g), receive, via a graphical user interface (GUI), auser selection of the labeled region of the inter-segmented vertebra mapthat corresponds to the vertebral centrum; and (i) determine a vertebralcentrum region of the inter-segmented vertebra map, the vertebralcentrum region corresponding to the user selection [e.g., (A) bylabeling the user selected labeled region as corresponding to thevertebral centrum (e.g., and labeling the remaining labeled regions ascorresponding to other regions of the vertebra), thereby producing alabeled vertebral centrum map (e.g., a binary map) that differentiates aregion of the single vertebra mask corresponding to the vertebralcentrum from other regions of the single vertebral mask; e.g., (B) bygenerating a vertebral centrum mask that identifies the labeled regionselected by the user].

In certain embodiments, the instructions cause the processor todetermine one or more morphometric measurements (e.g., for diagnosticpurposes; e.g., for determining treatment efficacy) using the determinedvertebral centrum region.

In certain embodiments, the one or more morphometric measurementscomprise measurements of one or more morphometric attributes of atrabecular and/or cortical component of the vertebral centrum (e.g., avolume of a trabecular component of the vertebral centrum).

In certain embodiments, the 3D image of the subject is a CT image (e.g.,a microCT image).

BRIEF DESCRIPTION OF THE FIGURES

The foregoing and other objects, aspects, features, and advantages ofthe present disclosure will become more apparent and better understoodby referring to the following description taken in conjunction with theaccompanying drawings, in which:

FIG. 1A is a gray scale microCT image of three lumbar vertebrae from amurine model.

FIG. 1B is a gray scale microCT image of three lumbar vertebrae from amurine model with volumes of interest (VOI) boundaries drawn (manually)around vertebral centrums of the three lumbar vertebrae.

FIG. 2A is an image corresponding to a rostral view of a mouse T2vertebra [adapted from (I. A. Bab, C. Hajbi-Yonissi, Y. Gabet, and R.Müller, Micro-Tomographic Atlas of the Mouse Skeleton, New York, N.Y.,USA. Springer, 2007; pg. 68)].

FIG. 2B is an image corresponding to an internal view of a mouse T2vertebra [adapted from (I. A. Bab, C. Hajbi-Yonissi, Y. Gabet, and R.Müller, Micro-Tomographic Atlas of the Mouse Skeleton, New York, N.Y.,USA. Springer, 2007; pg. 70)].

FIG. 3 is an image showing a representation of a labeled (segmented)bone map that distinguishes individual bones, including three labeledregions that identify and differentiate between three lumbar vertebrae,according to an illustrative embodiment.

FIG. 4 is an image showing a representation of a labeled inter-segmentedvertebra map determined using the approaches described herein, accordingto an illustrative embodiment.

FIG. 5 is a block flow diagram of a process for automated detection andsegmentation of vertebral centrums, according to an illustrativeembodiment.

FIG. 6 is a block flow diagram of a process for automated detection andsegmentation of vertebral centrums, according to an illustrativeembodiment.

FIG. 7A is an image showing a representation of a filled single vertebramask determined using the approaches described herein, according to anillustrative embodiment.

FIG. 7B is an image showing a representation of a cross-section of afilled single vertebra mask determined using the approaches describedherein, according to an illustrative embodiment.

FIG. 8A is an image showing a representation of a result of applying amorphological dilation operation to a single vertebra mask, according toan illustrative embodiment.

FIG. 8B is an image showing a representation of a result of applying amorphological filling operation to a single vertebra mask, according toan illustrative embodiment.

FIG. 9 is an image showing a representation of a distance map determinedusing the approaches described herein, according to an illustrativeembodiment.

FIG. 10 is a block diagram of an exemplary cloud computing environment,used in certain embodiments.

FIG. 11 is a block diagram of an example computing device and an examplemobile computing device used in certain embodiments.

The features and advantages of the present disclosure will become moreapparent from the detailed description set forth below when taken inconjunction with the drawings, in which like reference charactersidentify corresponding elements throughout. In the drawings, likereference numbers generally indicate identical, functionally similar,and/or structurally similar elements.

DEFINITIONS

In this application, the use of “or” means “and/or” unless statedotherwise. As used in this application, the term “comprise” andvariations of the term, such as “comprising” and “comprises,” are notintended to exclude other additives, components, integers or steps. Asused in this application, the terms “about” and “approximately” are usedas equivalents. Any numerals used in this application with or withoutabout/approximately are meant to cover any normal fluctuationsappreciated by one of ordinary skill in the relevant art. In certainembodiments, the term “approximately” or “about” refers to a range ofvalues that fall within 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%,12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, or less in eitherdirection (greater than or less than) of the stated reference valueunless otherwise stated or otherwise evident from the context (exceptwhere such number would exceed 100% of a possible value).

Image: As used herein, the term “image”, for example, as in athree-dimensional image of a mammal, includes any visual representation,such as a photo, a video frame, streaming video, as well as anyelectronic, digital, or mathematical analogue of a photo, video frame,or streaming video. Any apparatus described herein, in certainembodiments, includes a display for displaying an image or any otherresult produced by a processor. Any method described herein, in certainembodiments, includes a step of displaying an image or any other resultproduced by the method.

3D, three-dimensional: As used herein, “3D” or “three-dimensional” withreference to an “image” means conveying information about three spatialdimensions. A 3D image may be rendered as a dataset in three dimensionsand/or may be displayed as a set of two-dimensional representations, oras a three-dimensional representation. In certain embodiments, a 3-Dimage is represented as voxel (e.g., volumetric pixel) data.

Various medical imaging devices and other 3-D imaging devices (e.g., acomputed tomography scanner (CT scanner), a microCT scanner, etc.)output 3-D images comprising voxels or otherwise have their outputconverted to 3-D images comprising voxels for analysis. In certainembodiments, a voxel corresponds to a unique coordinate in a 3-D image(e.g., a 3-D array). In certain embodiments, each voxel exists in eithera filled or an unfilled state (e.g., binary ON or OFF).

Mask: As used herein, a “mask” is a graphical pattern that identifies a2D or 3D region and is used to control the elimination or retention ofportions of an image or other graphical pattern. In certain embodiments,a mask is represented as a binary 2-D or 3-D image, wherein each pixelof a 2-D image or each voxel of a 3-D image is assigned one of twovalues of a binary set of values (e.g. each pixel or voxel may beassigned a 1 or a 0, e.g. each pixel or voxel may be assigned a Boolean“true” or “false” value).

Second derivative splitting filter: As used herein, applying a “secondderivative splitting filter” is an image processing operation based onthe second derivatives (or approximations thereof) of the intensity of a3D image, e.g., a gray-scale 3D image, at each of a plurality of voxels.In some embodiments, a splitting filter is derived from Gaussian secondderivative filters selected from Laplacian of Gaussian (LoG), highestHessian eigenvalue with preliminary Gaussian filtering (HEH), and lowestHessian eigenvalue with preliminary Gaussian filtering (LEH).

Split-line voxels: As used herein, the terms “split-line voxels” referto voxels of a given image and/or mask that are identified and used toremove voxels from a particular mask, thereby splitting the particularmask.

Seed: As used herein, the term “seed” refers to a set of voxels (e.g., aconnected set of voxels) that is used as an initial starting region fora growing operation that expands the size of the seed until a particularstop criteria is met. In certain embodiments, the growing operationexpands the size of the seed by repeatedly adding to it neighboringvoxels.

Label: As used herein, the term “label” refers to an identifier (e.g., acomputer representation of an identifier, such as a textual value, anumeric value, a Boolean value, and the like) that is linked to aspecific region of an image.

Subject: As used herein, the term “subject” refers to an individual thatis imaged. In certain embodiments, the subject is a human. In certainembodiments, the subject is a small animal.

Small animal: As used herein, a “small animal” refers to small mammalsthat can be imaged with a microCT and/or micro-MR imager. In someembodiments, “small animal” refers to mice, rats, voles, rabbits,hamsters, and similarly-sized animals.

Bone, bone tissue: As used herein, the terms “bone” and “bone tissue”refer to any osseous tissue, and include, for example, both normalskeleton and heterotopic ossification (HO).

Vertebra portion(s): As used herein, the term “vertebra portion” refersto a portion of an individual vertebra, including up to all of theindividual vertebra (e.g., a vertebra portion may be an entireindividual vertebra).

Link: As used herein, the terms “link”, and “linked”, as in a first datastructure or data element is linked to a second data structure or dataelement, refer to a computer representation of an association betweentwo data structures or data elements that is stored electronically (e.g.in computer memory).

Provide: As used herein, the term “provide”, as in “providing data”,refers to a process for passing data in between different softwareapplications, modules, systems, and/or databases. In certainembodiments, providing data comprises the execution of instructions by aprocess to transfer data in between software applications, or in betweendifferent modules of the same software application. In certainembodiments a software application may provide data to anotherapplication in the form of a file. In certain embodiments an applicationmay provide data to another application on the same processor. Incertain embodiments standard protocols may be used to provide data toapplications on different resources. In certain embodiments a module ina software application may provide data to another module by passingarguments to that module.

DETAILED DESCRIPTION

It is contemplated that systems, architectures, devices, methods, andprocesses of the claimed invention encompass variations and adaptationsdeveloped using information from the embodiments described herein.Adaptation and/or modification of the systems, architectures, devices,methods, and processes described herein may be performed, ascontemplated by this description.

Throughout the description, where articles, devices, systems, andarchitectures are described as having, including, or comprising specificcomponents, or where processes and methods are described as having,including, or comprising specific steps, it is contemplated that,additionally, there are articles, devices, systems, and architectures ofthe present invention that consist essentially of, or consist of, therecited components, and that there are processes and methods accordingto the present invention that consist essentially of, or consist of, therecited processing steps.

It should be understood that the order of steps or order for performingcertain action is immaterial so long as the invention remains operable.Moreover, two or more steps or actions may be conducted simultaneously.

The mention herein of any publication, for example, in the Backgroundsection, is not an admission that the publication serves as prior artwith respect to any of the claims presented herein. The Backgroundsection is presented for purposes of clarity and is not meant as adescription of prior art with respect to any claim.

Documents are incorporated herein by reference as noted. Where there isany discrepancy in the meaning of a particular term, the meaningprovided in the Definition section above is controlling.

Headers are provided for the convenience of the reader—the presenceand/or placement of a header is not intended to limit the scope of thesubject matter described herein.

Described herein are systems and methods for detection and segmentationof graphical representations of vertebral centrums within 3D images. Incertain embodiments, the systems and methods described herein provide atool that receives a 3D image of a subject and uses a combination ofimage processing operations to identify regions of the 3D image thatcorrespond to graphical representations of vertebral centrums ofindividual vertebrae.

In certain embodiments, the approaches described herein operate onsingle vertebra masks that identify individual vertebrae in order tofurther segment each single vertebra mask into a plurality of discreteand distinguishable sub-regions, including a vertebral centrumsub-region (e.g., a single vertebral centrum sub-region). In thismanner, the vertebral centrum segmentation approaches described hereingenerate, from a single vertebra mask, a labeled inter-segmentedvertebra mask in an automated fashion. The labeled inter-segmentedvertebra mask comprises multiple labeled regions, one of whichcorresponds to a vertebral centrum region. The vertebral centrum regionmay be classified (e.g., classified as corresponding to a vertebralcentrum) automatically or may be classified manually, for example via auser interaction.

For example, FIG. 3 shows a labeled (segmented) bone map 300 generatedfrom the 3D microCT image shown in FIG. 1A and FIG. 1B. The labeled(segmented) bone map 300 comprises a plurality of labeled regions thatdistinguish between individual bones, including three labeled regions302, 304, and 306 that identify and differentiate between three lumbarvertebrae. As described herein, a particular labeled regioncorresponding to an individual vertebra of interest may be selected andused to generate a single vertebra mask corresponding to the individualvertebra of interest.

The single vertebra mask is then analyzed to generate a labeledinter-segmented map that comprises a plurality of differentiable andlabeled regions, one of which corresponds to the vertebral centrum ofthe particular vertebra of interest. FIG. 4 shows a representation of alabeled inter-segmented vertebra map 400 generated for the middle (L4)vertebra identified via region 304 for FIG. 3. The inter-segmentedsingle vertebra map 400 includes a plurality of labeled sub-regions 402,404, 406, 408, and 410. Sub-region 404 corresponds to the vertebralcentrum. As described herein, once the labeled inter-segmented vertebramap 400 is generated, the sub-region corresponding to the vertebralcentrum 404 may be classified as such, either via further automatedprocessing or manually, via a simple streamlined user interaction suchas a single ‘affirmative’ click on the vertebral centrum region in arepresentation rendered in a graphical user interface (GUI).

As described herein, ensuring generation of a labeled inter-segmentedvertebra map that includes a sub-region that accurately and consistentlyidentifies a vertebral centrum sub-region is non-trivial. In particular,the vertebral centrum segmentation approaches described herein utilize aseries of image processing steps that account for and leverage insightabout the specific physical structure of individual vertebrae andvertebral centrums thereof as shown, for example, in FIG. 2A and FIG.2B.

In particular, the vertebral centrum segmentation approaches describedherein utilize a filling step that artificially fills in regions of asingle vertebra mask that correspond to perforations and interior (e.g.,trabecular) regions, such as region 252 in FIG. 2B (not to be confusedwith the neural canal 204). This approach allows a vertebral centrumregion 202 of a representation of an individual vertebra to beidentified via distance transform and watershed segmentation steps thatleverage the narrow connections (e.g., ‘necks’) between the vertebralcentrum and other regions of the individual vertebra. As describedherein, the distance transform and watershed segmentation steps providefor separation of the vertebral centrum region from the other portionsof the vertebra representation via identification of these narrowconnections (e.g., ‘necks’), while the filling step avoidsover-segmentation errors that would otherwise result due to, forexample, lower densities and fine sub-structure of interior trabecularregions such as those shown in FIG. 2B that cause the initially obtained(e.g., accessed; e.g., generated) single vertebra masks to have hollow,shell-like structures.

In particular, the vertebral centrum segmentation approaches describedherein utilize a filling step that artificially fills in regions of asingle vertebra mask that correspond to perforations and interior (e.g.,trabecular) regions, such as region 252 in FIG. 2B (not to be confusedwith the neural canal 204). This approach allows a vertebral centrumregion 202 of a representation of an individual vertebra to beidentified via distance transform and watershed segmentation steps thatleverage the narrow connections (e.g., ‘necks’) between the vertebralcentrum and other regions of the individual vertebra. As describedherein, the distance transform and watershed segmentation steps providefor separation of the vertebral centrum region from the other portionsof the vertebra representation via identification of these narrowconnections (e.g., ‘necks’), while the filling step avoidsover-segmentation errors that would otherwise result due to, forexample, lower densities and fine sub-structure of interior trabecularregions such as those shown in FIG. 2B that cause the initially obtained(e.g., accessed; e.g., generated) single vertebra masks to have hollow,shell-like structures.

FIG. 5 shows an example process 500 for detecting and segmentingvertebral centrums of individual vertebra in images. The process 500begins by receiving a 3D image of a subject 502, such as a 3D microCTimage. In certain embodiments, the 3D microCT image comprises aplurality of voxels, each of which represents a specific 3D volumewithin a region of the imaged subject. Each voxel of the 3D image has anintensity value that provides a measure of contrast, as detected via theparticular imaging modality used to obtain the 3D image. For example,voxel intensities of 3D microCT images may be represented usingHounsfield unit values, which provide a measure of attenuation thatX-rays experience when passing through various regions of the subjectbefore they are detected by an X-ray detector of the microCT detector.

In certain embodiments, the region of the subject that is imagedcomprises various bones, including individual vertebra portions.Accordingly, the received 3D image comprises graphical representationsof (e.g., among other things) one or more individual vertebra portions.As described herein, FIG. 1A and FIG. 1B show images of microCT imagesof three lumbar vertebrae of murine models. In the figures, the darkgray regions correspond to graphical representations of bone. Specificregions of the image shown in FIG. 1B corresponding to vertebralcentrums of individual vertebrae, having been identified manually (e.g.,via a user manually drawing on the image), are outlined in the figure.

A. Segmentation of Individual Vertebra(e) and Single Vertebra Mask(s)

Returning to FIG. 5, in another step 504, a single vertebra mask 506 isaccessed and/or generated. For example, the systems and methodsdescribed herein may access and operate on an already generated singlevertebra mask, which is then further segmented as described herein ormay include steps to generate the single vertebra mask that is furthersegmented to generate the inter-segmented vertebra mask.

The single vertebra mask 506 is a mask that identifies a portion of the3D image that is determined as corresponding to a particular vertebra ofinterest, the vertebral centrum of which is to be identified andsegmented. For example, the single vertebra mask 506 may be a binarymask comprising a plurality of voxels, each corresponding to a voxel ofthe 3D image. Voxels that are identified as corresponding to theparticular vertebra are assigned a first value, such as a numeric 1 or aBoolean ‘true’, while other voxels are assigned a second value, such asa numeric 0 or a Boolean ‘false’.

In certain embodiments, the single vertebra mask 506 is an alreadygenerated single vertebra mask and step 504 comprises accessing thealready generated single vertebra mask. Such a single vertebra mask maybe, for example, stored in memory and accessed.

In certain embodiments, step 504 includes generating the single vertebramask 506. A variety of approaches may be used for generating a singlevertebra mask, including manual identification of the particular singlevertebra of interest, such as via a user interaction wherein a usermanually draws boundaries of a particular vertebra of interest.

In certain embodiments, a more streamlined and robust approach isutilized wherein individual bones (including, but not limited toindividual vertebra portions) are identified within the image using anautomated segmentation approach.

For example, FIG. 6 shows a specific embodiment of a process 600 forvertebral centrum detection and segmentation that includes additionalsteps for generating the single vertebra mask 506. The additional steps,in certain embodiments, are used to automatically segment individualbones represented in a 3D image. In the embodiment shown in FIG. 6, athresholding operation is applied 602 to the 3D image to generate abinary bone mask 604 that identifies regions of the 3D image thatcorrespond to bone. Voxels of the binary bone mask may, for example, beassigned a first or second value based on whether an intensity of acorresponding voxel of the 3D image 502 is above or below a particularthreshold value. The thresholding operation 602 may use a same, singlethreshold as the particular threshold with which the intensity of eachvoxel of the 3D image is compared, or may select the particularthreshold from multiple thresholds, such as in a hysteresis thresholdingapproach. In certain embodiments, when an intensity of a voxel of the 3Dimage is above the particular threshold, it is identified as bone and acorresponding voxel of the binary bone mask 602 is assigned the firstvalue (e.g., a numeric 1; e.g., a Boolean ‘false’), and when anintensity of a voxel of the 3D image is below the particular threshold,it is identified as not corresponding to bone and corresponding voxel ofthe binary bone mask 602 is assigned the second value (e.g., a numeric0; e.g., a Boolean ‘false’). Thresholding approaches for generatingmasks that distinguish bone voxels from non-bone voxels in 3D images aredescribed in greater detail in U.S. patent application Ser. No.14/812,483, filed Jul. 29, 2015; PCT Application PCT/US15/42631, filedJul. 29, 2015; and U.S. patent application Ser. No. 15/604,350, filedMay 24, 2017, the contents of each of which are hereby incorporated byreference in their entirety.

The binary bone mask 604 may then be split into multiple regions, eachcorresponding to a different individual bone, via a bone separation step606. The different regions may be distinguishably labeled to generate alabeled (segmented) bone map 608 that differentiates between regions ofthe graphical representation that correspond to different individualbones.

In certain embodiments, the bone separation step 606 comprises applyingone or more second derivative filters to the 3D image, for example as inthe bone separation approach described in U.S. patent application Ser.No. 14/812,483, filed Jul. 29, 2015; and PCT Application PCT/US15/42631,filed Jul. 29, 2015. In particular, in such an approach, one or moresecond derivatives may be applied to the 3D image to produce a splitbone mask for the image with bone boundaries removed. Morphologicalprocessing operations may be performed to determine split binarycomponents of the split bone mask, which can then be used as seeds for aregion growing operation that to produce the labeled (segmented) bonemap (referred to as a “segmentation map” in U.S. patent application Ser.No. 14/812,483, filed Jul. 29, 2015; and PCT Application PCT/US15/42631,filed Jul. 29, 2015) comprising a plurality of labeled regions thatdifferentiate individual bones in the 3D image.

Following generation of the labeled (segmented) bone map 608, aparticular labeled region that corresponds to the particular individualvertebra of interest may be selected 610 and used to generate the singlevertebra mask 506. The region corresponding to the particular individualvertebra may be selected automatically, or based on input from a user ina semi-automated fashion. For example, a graphical representation of thelabeled (segmented) bone map may be rendered for display to the user.The differently labeled regions in the rendered graphical representationmay be visually distinguished, for example via different colors,grayscale shadings, and the like. The user may then simply identify aparticular region that corresponds to an individual vertebra ofinterest, by, for example, via a ‘click’ (e.g., with a mouse) or ‘tap’(e.g., using a touch sensitive interface). A mask that identifies thisregion may then be generated and used as the single vertebra mask 506.In this manner, a user may selected a particular individual vertebra forsegmentation and/or analysis via a single quick ‘click’ or ‘tap’ withina graphical user interface (GUI).

B. Detection and Segmentation of Vertebral Centrum Regions

In certain embodiments, once a single vertebra mask 506 is obtained(e.g., either accessed or generated by the systems and methods describedherein), the vertebral centrum segmentation approaches described hereinoperate on the single vertebra mask 506 to generate a labeledinter-segmented vertebra map 522, such as the example shown in FIG. 4.

Separating a vertebral centrum region from other regions of the singlevertebra mask that correspond to other portions of an individualvertebra is non-trivial. The approaches described herein includespecific processing steps that both take advantage of physical featuresof individual vertebrae and also address image processing challengesthat certain features present.

B.i Filled Single Vertebra Mask Generation

In certain embodiments, the approaches described herein compriseperforming one or more morphological operations to fill in perforationsand/or interior regions of the single vertebra mask 508, therebygenerating a filled single vertebra mask 510. This filling step 508addresses the image processing challenges presented by structuralfeatures of individual vertebrae, in particular their interiortrabecular regions as well as blood vessels and other fine structurethat run through and create openings the outer, cortical shell of thevertebral centrum.

As shown in FIG. 3A and FIG. 3B, the vertebral centrum corresponds to acylindrical region of an individual vertebra connected to other regionsof the individual vertebra by comparatively narrow structures. Theinterior of the vertebra, however, is not solid, dense bone, and insteadcomprises marrow, soft-tissue, and various other fine structure, asshown in FIG. 2B. As a result of the different densities of the outerand interior portions of vertebrae, the outer and interior portions ofvertebrae are manifest as different gray-scale intensities in microCTimages (e.g., the interior, soft-tissue regions having a lower intensityvalue, representative of less dense tissue). In turn, single vertebramasks generated from such images are not solid, but rather areshell-like, and comprise hollow interior regions (e.g., interior voxelslabeled as numeric ‘0’ or Boolean false values).

By filling these interior regions to generate a filled single vertebramask 510, the approaches described herein transform the hollow,shell-like single vertebra mask 506 into a solid structure. Performingsubsequent distance transform 512 and watershed segmentation operations516 allows for separation of the vertebral centrum region from othersub-regions of an individual vertebra based on its relative thickness inthe filled single vertebra mask in comparison with portions of the maskthat join it with the other sub-regions.

Notably, distance transforms serve to identify thin structures (e.g.,‘necks’) in masks by determining distances from each voxel of a mask toa nearest boundary (e.g., to a nearest numeric ‘0’ or Boolean ‘false’valued voxel). Accordingly, generating a filled single vertebra mask andperforming a subsequent distance transform 512 using the filled singlevertebra mask avoids severe over-segmentation errors that would resultwere the distance transform applied instead to a hollow, shell-likesingle vertebra mask as initially accessed and/or generated. Suchover-segmentation errors would, for example, instead of the single,easily identified vertebral centrum region 404 of the example in FIG. 4,result in a plurality of smaller, potentially ambiguous sub-regions.

In certain embodiments, the filling step 508 used to generate the filledsingle vertebra mask 510 is accomplished using a morphological dilationoperation 632 and a morphological hole filling operation 634, as shownin the detailed example process 600 of FIG. 6. A morphological dilation632 is performed to grow the single vertebra mask 506 and fill inperforations in it and generate a dilated single vertebra mask. Suchperforations typically correspond to small holes running from theinterior to the exterior of the shell-like single vertebra mask 506.These perforations result from physical structures such as blood vesselsthat run from the interior (e.g., marrow portion) of vertebra to theexterior. Accordingly, the morphological dilation operation 632 may usea dilation element with a size based on sizes of such physicalstructures, such as blood vessels, usually responsible for perforationsin individual vertebra. For example, the size of the dilation element invoxels may be determined (e.g., automatically) to correspond (e.g., beapproximately greater than or equal to) a particular physical sizeassociated with blood vessels, based on a resolution of the 3D image502. For example, for a 3D image with a resolution of approximately 20to 30 μm along each dimension per voxel, a dilation element with a sizeof 5 to 8 voxels along one or more dimensions would be used (e.g.,corresponding to a physical size of approximately 100 to 240 μm alongone or more dimensions).

In certain embodiments, the size of the dilation element may be auser-exposed parameter, that the user can adjust themselves. This may beuseful to account for certain cases where unusually large perforationsare present, for example due to tumors and/or cracks in vertebrae. Auser may increase or input a specific value for a size of the dilationelement to account for such features.

In certain embodiments, a morphological hole filling operation 634 isperformed to fill in one or more interior regions in the dilated singlevertebra mask generated following the morphological dilation operation632. The morphological hole filling operation 634 thus fills in theinterior regions of the single vertebra mask that correspond physicallyto the marrow and soft-tissue interior regions of the particularindividual vertebra that it represents. In certain embodiments, it isnecessary to first eliminate perforations in the single vertebra maskvia the morphological dilation operation 632 prior to performing themorphological hole filling operation 634. In particular, certainmorphological hole filling operations may fail when applied to maskswith perforations that prevent interior regions from being well-defined.FIG. 8A shows an example dilated single vertebra mask following amorphological dilation operation, and FIG. 8B shows an example filledsingle vertebra mask following a morphological hole filling operation.

In certain embodiments, the filled single vertebra mask generated byapplying the morphological dilation and hole filling operations isrefined using a morphological erosion operation 636. Since, in additionto filling in perforations, the morphological dilation operation growsthe single vertebra mask outwards, a morphological erosion operation 636performed using an erosion element with a size that is approximately thesame as that of the dilation element can be used to undo this growingeffect.

Accordingly, by filling in perforations and/or interior regions of theshell-like single vertebra mask 506 as described herein, a filled singlevertebra mask 510 corresponding to a filled, solid object can begenerated. FIG. 7A and FIG. 7B show a representation of a filled singlevertebra mask generated by the example process 600 shown in FIG. 6,following the morphological erosion step 636. FIG. 7B shows across-sectional cut through the filled single vertebra mask shown inFIG. 7A. As shown in the figure, the interior of the single vertebramask is solid and filled in—the single ‘hole’ corresponds physically tothe neural canal 204 of the physical vertebra it represents. The finestructure and interior regions, such as 252 shown in FIG. 2B, of thephysical individual vertebra that the filled single vertebra maskrepresents to are absent, having been filled in via the fillingapproaches described above.

In certain embodiments, additional steps are performed, for exampleprior to the filling step 508. For example, as shown in process 600 ofFIG. 6, optional auto-crop 620 and/or fill image border 622 steps may beperformed. An auto-crop step 620 crops the 3D image to the local regionsurrounding the single vertebra mask. Reducing the image size in thismanner can, for example, increase speed of downstream processing steps.A fill image border step 622 may be included when the single vertebramask identifies a particular individual vertebra that is partially outof view. In this case, a portion of the single vertebra mask lies on aborder of the 3D image. Similar to the manner in which, in certainembodiments, perforations in the single vertebra mask need to be filledin (e.g., via a morphological dilation operation) prior to performing amorphological hole filling step, open regions in the cross-section ofthe single vertebra mask lying on the image border are filled via thefill image border step 622, thereby ‘capping’ an open end of the singlevertebra mask on the image border.

B.ii Distance Transform and Distance Map Determination

In certain embodiments, process 500 comprises a step of applying adistance transform 512 to the filled single vertebra mask 510 todetermine a distance map 514. The distance transform determines, foreach voxel of the filled single vertebra mask corresponding to bone[e.g., assigned the first value (e.g., numeric 1; e.g., Boolean‘false’)] a distance from that voxel to a nearest boundary orsoft-tissue region of the 3D image [e.g., a distance to a nearest voxelof the filled single vertebra mask having the second value (e.g.,numeric 0; e.g., Boolean ‘false’)]. The distance transform thus producesa distance map 514, which comprises a plurality of voxels, each of whichcorresponds to a voxel of the filled single vertebra mask 510 and has(e.g., is assigned) a distance value that represents a distance from thevoxel to a nearest boundary and/or non-bone voxel (e.g., a voxel of thefilled single vertebra mask having a value of 0).

An example distance map determined by applying a distance transform tothe filled single vertebra mask of FIG. 7A is shown in FIG. 9. Values ofthe distance map voxels are represented in gray-scale and using contourlines. Regions inside contour lines correspond to thicker regions thanthose outside the contour lines. Outside contour lines, shading fromdark to light gray indicates decreasing thickness (e.g., decreasingdistance from a boundary), with white representing 0 distance.Accordingly, the thickest regions of bone, corresponding primarily tothe vertebral centrum region, are shown within the large centralcontour, and the thinnest regions, such as the pedicles, in the imageare shown as outside the contour lines, and fading to white as distanceapproaches 0.

B.iii Watershed Segmentation

In certain embodiments, once the distance map is determined, a watershedsegmentation step 516 applied to the distance map 514. The watershedsegmentation step 516 includes a watershed segmentation operation, suchas H-extrema watershed segmentation, that identifies a set of catchmentbasins 518 and/or watershed lines within the distance map. Catchmentbasins 518 of the distance map correspond to thicker regions of bone,represented by larger distance values within the distance map. Catchmentbasins 518 are separated from each other by watershed lines thatcorrespond to connected lines of voxels that correspond to narrowconnectors. Accordingly, the thick, solid vertebral centrum region ofthe filled single vertebra mask 510 is represented by one catchmentbasin, while regions corresponding pedicles and other structures of theparticular individual vertebra that are attached to the vertebralcentrum via narrow connections are represented by other catchmentbasins.

In certain embodiments, the watershed segmentation operation partitionsthe distance map into a plurality of catchment basins that are separatedfrom each other by watershed lines. In certain embodiments, thewatershed segmentation operation produces a watershed mask comprising aplurality of catchment basins (e.g., each catchment basin correspondingto a connected region of voxels assigned a first value such as a numeric1 or Boolean ‘true’) separated from each other by watershed lines (e.g.,each watershed line corresponding to a connected line of voxels assigneda second value, such as a numeric 0 or Boolean ‘true’).

B.iv Masking and Labeled Inter-Segmented Vertebra Map Generation

In certain embodiments, a masking step 520 uses the set of catchmentbasins 518 generated via the watershed segmentation step 516 along withthe single vertebra mask 506 to generate the inter-segmented vertebramap 522. The masking step 520 comprises identifying portions of thesingle vertebra mask lying within different catchment basins of the set518 and labeling them accordingly, in order to distinguish them fromeach other. For example, a particular portion of the single vertebramask 506 that lies within a particular catchment basin may be identifiedby taking a voxel-wise logical AND between the single vertebra mask 506and the particular catchment basin, and labeling the result [e.g.,assigning each voxel having a first value (e.g., a numeric 1; e.g., aBoolean ‘true) a label value (e.g., a particular integer value)]. Thisprocess may be repeated for each catchment basin of the set 518,labeling each result differently to distinguish the different regions ofthe single vertebra mask 506. In this manner, a labeled inter-segmentedvertebra map, such as the example 400 shown in FIG. 4, is generated. Asdescribed herein, the labeled inter-segmented vertebra map 522corresponds to a labeled version of the single vertebra mask 506 inwhich portions of the single vertebra mask 506 lying within differentcatchment basins of the set of catchment basins are identified andlabeled accordingly. By virtue of the combination(s) of processingoperations described herein, the vertebral centrum may be representedvia a single, easily identified labeled region in the inter-segmentedvertebra map.

C. Additional Processing

C.i User Interaction for Classification of Vertebral Centrum Sub-Region

In certain embodiments, once the labeled inter-segmented vertebra map522 is generated, a graphical representation of the labeledinter-segmented vertebra map 522 is rendered 524 for presentation to auser, for example within a graphical user interface (GUI). The user maythen select, via the GUI, the region corresponding to the vertebralcentrum. Once the use selects, for example, which labeled region of thelabeled-inter-segmented vertebra map correspond to the vertebralcentrum, the region may be labeled as such (e.g., as corresponding tothe vertebral centrum). This approach may be used to produce, forexample, a binary labeled map that differentiates between a region ofthe 3D image corresponding to the vertebral centrum of the particularindividual vertebra of interest and other portions of the particularindividual vertebra of interest. Additionally or alternatively, avertebral centrum mask that identifies the vertebral centrum of theparticular individual vertebra of interest may be generated. Typically,as shown in the example inter-segmented vertebra map 400 in FIG. 4, thevertebral centrum is represented by a single readily identified region(404 in FIG. 4) that can be selected.

In this manner, the systems and methods described herein allow a user toidentify a vertebral centrum region of a particular individual vertebraof interest represented in a 3D image by simply selecting a particularregion of a displayed inter-segmented vertebra map as corresponding to avertebral centrum. In certain embodiments, this can be accomplished viaa single affirmative ‘click’ (e.g., with a mouse) or ‘tap’ (e.g., usinga touch sensitive interface) within a graphical user interface (GUI).Accordingly, the vertebral centrum detection and segmentation tooldescribed herein eliminates the cumbersome and laborious process of auser manually drawing boundaries to identify regions of an image thatcorrespond to vertebral centrum(s) of individual vertebra(e). Moreover,because the labeled regions of the labeled inter-segmented vertebra mapare automatically generated, errors and inconsistencies betweendifferent users are dramatically reduced (e.g., two or more users arealmost guaranteed to select a same region(s) as corresponding to avertebral centrum, but it is very unlikely for two or more users to drawexactly the same identical boundaries on an image).

C.ii Metric Determination

Accordingly, by providing a tool for automatically detecting andsegmenting vertebral centrum(s) of individual vertebra(e) in images of asubject, the systems and methods described herein facilitate thedetection and segmentation approaches described herein therebyfacilitate streamlined quantitative analysis of images of vertebra(e)for applications such as osteological research and disease/injurydiagnosis. In particular, the approaches described herein provide abasis for analysis of morphometric attributes, density, and structuralparameters of vertebral centrum regions of individual vertebra(e). Asdescribed herein, such analysis is can provide insight useful fordeveloping understanding of disease and/or injury diagnosis, state, andprogression in a subject, as well as analysis of efficacy of differenttreatments.

For example, once the vertebral centrum region of the labeledinter-segmented vertebra map is identified, it can be used (e.g., as amask) to compute one or more morphometric measurements such as a volumeor surface (e.g., surface area) of the vertebral centrum. Othermeasurements, such as a connectivity, may also be determined. In certainembodiments, the identified vertebral centrum region is used todetermine regions of the image corresponding to a trabecular and/or acortical component of the vertebral centrum. Morphometric measurements,such as volume, surface (e.g., surface area), and the like, may thus beobtained for these specific components as well. For example, theidentified vertebral centrum region may be used to determine atrabecular component sub-region corresponding to the trabecularcomponent of the vertebral centrum. The volume of the trabecularcomponent sub-region can be determined (e.g., automatically) to measuretrabecular volume of the vertebral centrum. Automatically quantifyingtrabecular volume in this manner can provide insight into efficacy ofdifferent treatments for vertebral osteoporosis.

As described herein, since the vertebral centrum sub-region is generatedautomatically, and user interaction is limited to, at most, merelyidentifying (e.g., via selection) the vertebral centrum sub-region,inter and intra user errors and variations in measurements of vertebralcentrum morphometric attributes are reduced dramatically. The vertebralcentrum detection and segmentation approach described herein thusprovides a valuable tool for assessing osteological disease state and/orprogression in a subject and for assessing treatment efficacy.

C.iii Imaging Modalities

While the images presented and analyzed via the approaches describedherein are microCT images, other imaging modalities may also be used.For example, the approaches described herein may also be used fordetection and segmentation of vertebral centrum(s) of individualvertebra(e) in MRI images, optical images, and other types of images. Inparticular, the vertebral centrum segmentation and detection tooldescribed herein may be used for analysis of any imaging modality thatallows imaging of vertebral bones and osseous tissue (e.g., any modalitythat provides sufficient contrast between osseous tissue and softtissue).

D. Computer Systems and Network Environment

As shown in FIG. 10, an implementation of a network environment 1000 foruse in providing systems and methods for automated detection andsegmentation of vertebral centrum(s) described herein is shown anddescribed. In brief overview, referring now to FIG. 10, a block diagramof an exemplary cloud computing environment 1000 is shown and described.The cloud computing environment 1000 may include one or more resourceproviders 1002 a, 1002 b, 1002 c (collectively, 1002). Each resourceprovider 1002 may include computing resources. In some implementations,computing resources may include any hardware and/or software used toprocess data. For example, computing resources may include hardwareand/or software capable of executing algorithms, computer programs,and/or computer applications. In some implementations, exemplarycomputing resources may include application servers and/or databaseswith storage and retrieval capabilities. Each resource provider 1002 maybe connected to any other resource provider 1002 in the cloud computingenvironment 1000. In some implementations, the resource providers 1002may be connected over a computer network 1008. Each resource provider1002 may be connected to one or more computing device 1004 a, 1004 b,1004 c (collectively, 1004), over the computer network 1008.

The cloud computing environment 1000 may include a resource manager1006. The resource manager 1006 may be connected to the resourceproviders 1002 and the computing devices 1004 over the computer network1008. In some implementations, the resource manager 1006 may facilitatethe provision of computing resources by one or more resource providers1002 to one or more computing devices 1004. The resource manager 1006may receive a request for a computing resource from a particularcomputing device 1004. The resource manager 1006 may identify one ormore resource providers 1002 capable of providing the computing resourcerequested by the computing device 1004. The resource manager 1006 mayselect a resource provider 1002 to provide the computing resource. Theresource manager 1006 may facilitate a connection between the resourceprovider 1002 and a particular computing device 1004. In someimplementations, the resource manager 1006 may establish a connectionbetween a particular resource provider 1002 and a particular computingdevice 1004. In some implementations, the resource manager 1006 mayredirect a particular computing device 1004 to a particular resourceprovider 1002 with the requested computing resource.

FIG. 11 shows an example of a computing device 1100 and a mobilecomputing device 1150 that can be used to implement the techniquesdescribed in this disclosure. The computing device 1100 is intended torepresent various forms of digital computers, such as laptops, desktops,workstations, personal digital assistants, servers, blade servers,mainframes, and other appropriate computers. The mobile computing device1150 is intended to represent various forms of mobile devices, such aspersonal digital assistants, cellular telephones, smart-phones, andother similar computing devices. The components shown here, theirconnections and relationships, and their functions, are meant to beexamples only, and are not meant to be limiting.

The computing device 1100 includes a processor 1102, a memory 1104, astorage device 1106, a high-speed interface 1108 connecting to thememory 1104 and multiple high-speed expansion ports 1110, and alow-speed interface 1112 connecting to a low-speed expansion port 1114and the storage device 1106. Each of the processor 1102, the memory1104, the storage device 1106, the high-speed interface 1108, thehigh-speed expansion ports 1110, and the low-speed interface 1112, areinterconnected using various busses, and may be mounted on a commonmotherboard or in other manners as appropriate. The processor 1102 canprocess instructions for execution within the computing device 1100,including instructions stored in the memory 1104 or on the storagedevice 1106 to display graphical information for a GUI on an externalinput/output device, such as a display 1116 coupled to the high-speedinterface 1108. In other implementations, multiple processors and/ormultiple buses may be used, as appropriate, along with multiple memoriesand types of memory. Also, multiple computing devices may be connected,with each device providing portions of the necessary operations (e.g.,as a server bank, a group of blade servers, or a multi-processorsystem). Thus, as the term is used herein, where a plurality offunctions are described as being performed by “a processor”, thisencompasses embodiments wherein the plurality of functions are performedby any number of processors (one or more) of any number of computingdevices (one or more). Furthermore, where a function is described asbeing performed by “a processor”, this encompasses embodiments whereinthe function is performed by any number of processors (one or more) ofany number of computing devices (one or more) (e.g., in a distributedcomputing system).

The memory 1104 stores information within the computing device 1100. Insome implementations, the memory 1104 is a volatile memory unit orunits. In some implementations, the memory 1104 is a non-volatile memoryunit or units. The memory 1104 may also be another form ofcomputer-readable medium, such as a magnetic or optical disk.

The storage device 1106 is capable of providing mass storage for thecomputing device 1100. In some implementations, the storage device 1106may be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. Instructions can be stored in an information carrier.The instructions, when executed by one or more processing devices (forexample, processor 1102), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices such as computer- or machine-readable mediums (forexample, the memory 1104, the storage device 1106, or memory on theprocessor 1102).

The high-speed interface 1108 manages bandwidth-intensive operations forthe computing device 1100, while the low-speed interface 1112 manageslower bandwidth-intensive operations. Such allocation of functions is anexample only. In some implementations, the high-speed interface 1108 iscoupled to the memory 1104, the display 1116 (e.g., through a graphicsprocessor or accelerator), and to the high-speed expansion ports 1110,which may accept various expansion cards (not shown). In theimplementation, the low-speed interface 1112 is coupled to the storagedevice 1106 and the low-speed expansion port 1114. The low-speedexpansion port 1114, which may include various communication ports(e.g., USB, Bluetooth®, Ethernet, wireless Ethernet) may be coupled toone or more input/output devices, such as a keyboard, a pointing device,a scanner, or a networking device such as a switch or router, e.g.,through a network adapter.

The computing device 1100 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 1120, or multiple times in a group of such servers. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 1122. It may also be implemented as part of a rack serversystem 1124. Alternatively, components from the computing device 1100may be combined with other components in a mobile device (not shown),such as a mobile computing device 1150. Each of such devices may containone or more of the computing device 1100 and the mobile computing device1150, and an entire system may be made up of multiple computing devicescommunicating with each other.

The mobile computing device 1150 includes a processor 1152, a memory1164, an input/output device such as a display 1154, a communicationinterface 1166, and a transceiver 1168, among other components. Themobile computing device 1150 may also be provided with a storage device,such as a micro-drive or other device, to provide additional storage.Each of the processor 1152, the memory 1164, the display 1154, thecommunication interface 1166, and the transceiver 1168, areinterconnected using various buses, and several of the components may bemounted on a common motherboard or in other manners as appropriate.

The processor 1152 can execute instructions within the mobile computingdevice 1150, including instructions stored in the memory 1164. Theprocessor 1152 may be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 1152may provide, for example, for coordination of the other components ofthe mobile computing device 1150, such as control of user interfaces,applications run by the mobile computing device 1150, and wirelesscommunication by the mobile computing device 1150.

The processor 1152 may communicate with a user through a controlinterface 1158 and a display interface 1156 coupled to the display 1154.The display 1154 may be, for example, a TFT (Thin-Film-Transistor LiquidCrystal Display) display or an OLED (Organic Light Emitting Diode)display, or other appropriate display technology. The display interface1156 may comprise appropriate circuitry for driving the display 1154 topresent graphical and other information to a user. The control interface1158 may receive commands from a user and convert them for submission tothe processor 1152. In addition, an external interface 1162 may providecommunication with the processor 1152, so as to enable near areacommunication of the mobile computing device 1150 with other devices.The external interface 1162 may provide, for example, for wiredcommunication in some implementations, or for wireless communication inother implementations, and multiple interfaces may also be used.

The memory 1164 stores information within the mobile computing device1150. The memory 1164 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 1174 may also beprovided and connected to the mobile computing device 1150 through anexpansion interface 1172, which may include, for example, a SIMM (SingleIn Line Memory Module) card interface. The expansion memory 1174 mayprovide extra storage space for the mobile computing device 1150, or mayalso store applications or other information for the mobile computingdevice 1150. Specifically, the expansion memory 1174 may includeinstructions to carry out or supplement the processes described above,and may include secure information also. Thus, for example, theexpansion memory 1174 may be provide as a security module for the mobilecomputing device 1150, and may be programmed with instructions thatpermit secure use of the mobile computing device 1150. In addition,secure applications may be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner

The memory may include, for example, flash memory and/or NVRAM memory(non-volatile random access memory), as discussed below. In someimplementations, instructions are stored in an information carrier. thatthe instructions, when executed by one or more processing devices (forexample, processor 1152), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices, such as one or more computer- or machine-readablemediums (for example, the memory 1164, the expansion memory 1174, ormemory on the processor 1152). In some implementations, the instructionscan be received in a propagated signal, for example, over thetransceiver 1168 or the external interface 1162.

The mobile computing device 1150 may communicate wirelessly through thecommunication interface 1166, which may include digital signalprocessing circuitry where necessary. The communication interface 1166may provide for communications under various modes or protocols, such asGSM voice calls (Global System for Mobile communications), SMS (ShortMessage Service), EMS (Enhanced Messaging Service), or MMS messaging(Multimedia Messaging Service), CDMA (code division multiple access),TDMA (time division multiple access), PDC (Personal Digital Cellular),WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS(General Packet Radio Service), among others. Such communication mayoccur, for example, through the transceiver 1168 using aradio-frequency. In addition, short-range communication may occur, suchas using a Bluetooth®, Wi-Fi™, or other such transceiver (not shown). Inaddition, a GPS (Global Positioning System) receiver module 1170 mayprovide additional navigation- and location-related wireless data to themobile computing device 1150, which may be used as appropriate byapplications running on the mobile computing device 1150.

The mobile computing device 1150 may also communicate audibly using anaudio codec 1160, which may receive spoken information from a user andconvert it to usable digital information. The audio codec 1160 maylikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 1150. Such sound mayinclude sound from voice telephone calls, may include recorded sound(e.g., voice messages, music files, etc.) and may also include soundgenerated by applications operating on the mobile computing device 1150.

The mobile computing device 1150 may be implemented in a number ofdifferent forms, as shown in the figure. For example, it may beimplemented as a cellular telephone 1180. It may also be implemented aspart of a smart-phone 1182, personal digital assistant, or other similarmobile device.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms machine-readable medium andcomputer-readable medium refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term machine-readable signal refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (LAN), a wide area network (WAN), and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

In some implementations, any modules described herein can be separated,combined or incorporated into single or combined modules. The modulesdepicted in the figures are not intended to limit the systems describedherein to the software architectures shown therein.

Elements of different implementations described herein may be combinedto form other implementations not specifically set forth above. Elementsmay be left out of the processes, computer programs, databases, etc.described herein without adversely affecting their operation. Inaddition, the logic flows depicted in the figures do not require theparticular order shown, or sequential order, to achieve desirableresults. Various separate elements may be combined into one or moreindividual elements to perform the functions described herein.

Throughout the description, where apparatus and systems are described ashaving, including, or comprising specific components, or where processesand methods are described as having, including, or comprising specificsteps, it is contemplated that, additionally, there are apparatus, andsystems of the present invention that consist essentially of, or consistof, the recited components, and that there are processes and methodsaccording to the present invention that consist essentially of, orconsist of, the recited processing steps.

It should be understood that the order of steps or order for performingcertain action is immaterial so long as the invention remains operable.Moreover, two or more steps or actions may be conducted simultaneously.

While the invention has been particularly shown and described withreference to specific preferred embodiments, it should be understood bythose skilled in the art that various changes in form and detail may bemade therein without departing from the spirit and scope of theinvention as defined by the appended claims.

What is claimed is:
 1. A system for automatically detecting andsegmenting a vertebral centrum of a particular vertebra in a 3D image ofa subject, the system comprising: a processor of a computing device; anda memory having instructions stored thereon, wherein the instructions,when executed by the processor, cause the processor to: (a) receive a 3Dimage of a subject, wherein the 3D image comprises a graphicalrepresentation of one or more vertebra portions of the subject; (b)access and/or generate a single vertebra mask that identifies a portionof the graphical representation determined as corresponding to theparticular vertebra; (c) apply a morphological dilation operation togrow the single vertebra mask, thereby generating a dilated singlevertebra mask; (d) apply a morphological hole filling operation to thedilated single vertebra mask to fill one or more interior regions withinthe dilated single vertebra mask to generate the filled single vertebramask; (e) determine a distance map by applying a distance transform tothe filled single vertebra mask; (f) apply a watershed segmentationoperation to the distance map to identify a set of catchment basins fromthe distance map; (g) determine, using the set of catchment basins andthe single vertebra mask, a labeled inter-segmented vertebra mapcomprising a plurality of labeled regions, one of which corresponds tothe vertebral centrum; and (h) render a graphical representation of thelabeled inter-segmented vertebra map.
 2. The system of claim 1, wherein,at step (b), the instructions cause the processor to segment the 3Dimage to generate the single vertebra mask.
 3. The system of claim 2,wherein the instructions cause the processor to segment the 3D image byapplying one or more second derivative splitting filters to the 3Dimage.
 4. The system of claim 1, wherein, at step (b), the instructionscause the processor to: segment the 3D image to generate a labeled bonemap comprising a plurality of labeled regions that differentiateportions of the graphical representation corresponding to individualbones; render a graphical representation of the labeled bone map;receive a user selection of at least one of the plurality of labeledregions; and generate the single vertebra mask from the user selectedlabeled region.
 5. The system of claim 1, wherein at least a portion ofthe single vertebra mask lies on an edge of the 3D image, and whereinthe instructions cause the processor to fill an interior of the portionof the single vertebra mask lying on the edge of the 3D image.
 6. Thesystem of claim 1, wherein the instructions cause the processor torefine the filled single vertebra mask by performing a morphologicalerosion operation.
 7. The system of claim 1, wherein the instructionscause the processor to perform the morphological dilation operationusing a dilation element having a preset and/or automatically determinedsize based on a resolution of the 3D image.
 8. The system of claim 1,wherein the instructions cause the processor to receive a user input ofa dilation element size value and use the user input dilation elementsize in the applying the morphological dilation operation.
 9. The systemof claim 1, wherein the 3D image of the subject is a CT image.
 10. Asystem for automatically detecting and segmenting a vertebral centrum ofa particular vertebra in a 3D image of a subject, the system comprising:a processor of a computing device; and a memory having instructionsstored thereon, wherein the instructions, when executed by theprocessor, cause the processor to: (a) receive a 3D image of a subject,wherein the 3D image comprises a graphical representation of one or morevertebra portions of the subject; (b) access and/or generate a singlevertebra mask that identifies a portion of the graphical representationdetermined as corresponding to the particular vertebra; (c) apply one ormore morphological operations to fill in perforations and/or one or moreinterior regions of the single vertebra mask, thereby generating afilled single vertebra mask; (d) determine a distance map by applying adistance transform to the filled single vertebra mask; (e) apply awatershed segmentation operation to the distance map to identify a setof catchment basins from the distance map; (f) determine, using the setof catchment basins and the single vertebra mask, a labeledinter-segmented vertebra map comprising a plurality of labeled regions,one of which corresponds to the vertebral centrum; (g) render agraphical representation of the labeled inter-segmented vertebra map;(h) receive, via a graphical user interface (GUI), a user selection ofthe labeled region of the inter-segmented vertebra map that correspondsto the vertebral centrum; and (i) determine a vertebral centrum regionof the inter-segmented vertebra map, the vertebral centrum regioncorresponding to the user selection.
 11. The system of claim 10, whereinthe instructions cause the processor to determine one or moremorphometric measurements using the determined vertebral centrum region.12. The system of claim 11, wherein the one or more morphometricmeasurements comprise measurements of one or more morphometricattributes of a trabecular and/or cortical component of the vertebralcentrum.
 13. The system of claim 10, wherein, at step (b), theinstructions cause the processor to segment the 3D image to generate thesingle vertebra mask.
 14. The system of claim 13, wherein theinstructions cause the processor to segment the 3D image by applying oneor more second derivative splitting filters to the 3D image.
 15. Thesystem of claim 10, wherein, at step (b), the instructions cause theprocessor to: segment the 3D image to generate a labeled bone mapcomprising a plurality of labeled regions that differentiate portions ofthe graphical representation corresponding to individual bones; render agraphical representation of the labeled bone map; receive a userselection of at least one of the plurality of labeled regions; andgenerate the single vertebra mask from the user selected labeled region.16. The system of claim 10, wherein at least a portion of the singlevertebra mask lies on an edge of the 3D image, and wherein theinstructions cause the processor to fill an interior of the portion ofthe single vertebra mask lying on the edge of the 3D image.
 17. Thesystem of claim 10, wherein the instructions cause the processor torefine the filled single vertebra mask by performing a morphologicalerosion operation.
 18. The system of claim 17, wherein the instructionscause the processor to perform the morphological erosion operation usingan erosion element having a preset and/or automatically determined sizebased on a resolution of the 3D image.
 19. The system of claim 17,wherein the instructions cause the processor to receive a user input ofa dilation element size value and using an erosion element having a sizevalue the same as the dilation element size value in applying themorphological erosion operation.
 20. The system of claim 10, wherein the3D image of the subject is a CT image.